Image classification based on sample projection jointly with incoherent robust adaptive dictionary pair learning

Analysis–synthesis dictionary learning, which jointly learns synthesis and analysis dictionaries, is widely used in image classification. However, the performance of their algorithms remains unsatisfactory, primarily because the salient features in the samples are not effectively captured and the data contain redundant information. To address such problems, we propose an image classification algorithm based on sample projection jointly with incoherent robust adaptive dictionary pair learning. In this paper, we first project the samples into a discriminative subspace via least squares regression. Second, we integrate the joint learning of coefficients and salient features, together with their constraints, into a single model for training. Later, we impose an [Formula: see text]-norm constraint on the analysis dictionary and introduce an analysis incoherence promoting function to constrain the synthesis dictionary. Finally, we incorporate adaptive reconstruction weight learning to preserve the local structure of the training samples. We evaluated our method on the AR, Extended Yale B, ORL, CMU PIE, GT and COIL 20 datasets, achieving recognition rates of 99.67%, 98.41%, 96.50%, 98.73%, 78.50% and 99.87%, respectively.